Method for Distribution of Phasor-Aided State Estimation to Monitor Operating State of Large Scale Power System and Method for Processing Defect Data in Mixed Distributed State Estimation by Using Same

This is a national stage application of PCT/KR2021/014721 filed on Oct. 20, 2021,which claims the benefit of and priority to Korean Patent Application No. 10-2020-0136965 filed on Oct. 21, 2020 and Korean Patent Application No. 10-2021-0140064 filed on Oct. 20, 2021, the entire contents of each of which are incorporated herein by reference for all purposes.

The present invention relates to a decentralization strategy of phasor-aided state estimation (PHASE) for monitoring the operation of large-scale power systems, and more specifically, to a decentralized PHASE using heterogeneous hybrid data obtained from both supervisory control and data acquisition (SCADA) systems and phasor measurement units (PMUs), and a bad data processing (BDP) method in hybrid distributed state estimation (H-DSE) algorithm using the same.

Conventional centralized state estimation (CSE) requires a one central coordinate system to manage, analyze, and process all big data measured from a large-scale power system. However, with the increase in the demand for electric power usage, power systems are expanding in size and complexity. Consequently, scalability issues arise, including rising data management and processing costs, and greater demands for high-performance communication infrastructure. Due to these issues, there has been a growing interest in decentralized state estimation (DSE) rather than CSE. The DSE algorithms have been proposed in many studies which can be categorized into two groups: hierarchical and fully distributed approaches. In hierarchical approaches, each local estimator acquires the state estimation (SE) result only in a sub-area. However, a single central estimator is still required to obtain global estimates by integrating with all the local SE results. In contrast, fully distributed approaches avoid the requirement for central coordination. Hence, fully distributed approaches have advantages in terms of high efficiency in data transmission and processing, reduced computation burden, data privacy and so on.

In recent research related to fully distributed approach, DSE algorithms such as the gossip-based, the Lagrangian relaxation, and the alternating direction method of multipliers (ADMM) have been employed.

The ADMM-based DSE operates under a global observability condition, rather than local observability. As a result, ADMM-based DSE requires fewer measurements for each sub-area compared to other DSE algorithms. In particular, the ADMM can effectively address local observability issues arising from the decentralization processes due to a reduction in measurements at boundary buses.

Meanwhile, PMUs can provide time-synchronized measurements based on the Global Positioning System (GPS) at short sampling intervals (60 Hz or 120 Hz in Korea) and thus allow for more accurate SE. However, SCADA measurements, which have been conventionally used for SE, are transmitted to an energy management system (EMS) once every 1-2 seconds and are not time-synchronized. Therefore, it is necessary to utilize precise PMU data to enhance the accuracy of SE. For this reason, extensive research has been conducted on hybrid SE in which two types of measurements with different characteristics are simultaneously used.

HSE can be classified into sequential and parallel approaches. The sequential approach performs SCADA-based SE initially and then refines the SE result by incorporating PMU measurements. On the other hand, the parallel approach performs SCADA-based SE and PMU-based SE simultaneously and integrate the two SE results. Compared to the sequential approach, which requires performing SE consecutively twice, the parallel approach offers a time-efficient alternative. However, to employ the parallel approach, it is imperative to ensure system observability globally solely using PMU data.

Meanwhile, regardless of the sequential or parallel approaches, conventional distributed HSE generally employs the largest normalized residual test (LNRT) method, which is a well-known BDP method. However, under conditions with multiple bad data (BD), the LNRT method has drawbacks of low accuracy in identifying BD, low computation efficiency, and an inability to determine the validity of critical measurements. In contrast, the phasor-aided state estimation (PHASE) method can identify and correct BDs by improving the aforementioned shortcomings through cross-validation between SCADA-based estimates and PMU-based estimates. However, the PHASE method has only been used in a centralized manner.

The present invention is directed to providing a decentralization strategy of HSE to monitor and analyze the system operations for distributed power systems based on SCADA measurements and time-synchronized PMU measurements.

The present invention is also directed to integrating a PHASE-based BDP method into a H-DSE algorithm based on SCADA and PMU measurements for effectively processing BD under multiple BD conditions.

The present invention is also directed to providing a decentralization strategy of the PHASE method to be integrated with H-DSE algorithm. This strategy addresses several challenges inconsistency between a state vector of SCADA-based DSE and one based on PMU measurements; the complexity of calculating a covariance matrix of estimated local state vectors; and the inconsistency between SCADA-based local gain matrices and PMU-based local gain matrices. Asa result, the PHASE method can be integrated into H-DSE algorithm.

To achieve the technical objectives, the present invention utilizes a local state vector extension and provides covariance matrices of expanded state vectors. These covariance matrices are employed to process BD in H-DSE algorithm using PHASE.

To monitor the operation of large-scale power systems, a decentralized PHASE (DPHASE) included the following stages: communicating information on location of metering devices and network topology; establishing the extended sets and the extended state vector for each sub-area on the basis of local measurements and communicated information; conducting SCADA-based DSE and PMU-based DSE in parallel using SCADA measurements and PMU measurements, respectively; integrating estimates from SCADA-based and PMU-based DSE algorithms; and performing both the phasor-aided normalized residual test and the general normalized residual test.

The parallel execution of the SCADA-based DSE and the PMU-based DSE involves estimating the local states of each sub-area using ADMM algorithm while interacting with adjacent local estimators.

After the performing of the phasor-aided normalized residual test and the general normalized residual test, the DPHASE method may determine whether there is BD in both the SCADA and PMU measurements for each sub-area.

If no BD is detected, the DPHASE method may further comprise fusing the results from the SCADA-based DSE and the PMU-based DSE algorithms.

Conversely, if BD is identified, the DPHASE method may further comprise removing the BD and recovering the data using a matrix completion method.

The DPHASE method may further comprise subsequently carrying out an additional DSE using the recovered SCADA and PMU measurements.

The additional DSE might generate new SCADA-based and PMU-based estimates by performing ADMM-based DSE in parallel.

After the performing of the additional DSE, the DPHASE method may further comprise fusing the results of the SCADA-based and the PMU-based DSE algorithms.

In accordance with another aspect of the present invention, a BDP method for monitoring the operating state of a large-scale power system within a H-DSE framework involves: mixing results from the SCADA-based and PMU-based DSE algorithms; performing both a phasor-aided normalized residual test and a general normalized residual test; determining the presence of BD in the SCADA and PMU measurements for each sub-area, which is distinguished or defined by a network topology related to the SCADA and the PMU measurements and position information of measurement equipment according to the network topology.

If no BD is detected, the BDP method may further comprise fusing the results from the SCADA-based DSE and the PMU-based DSE algorithms.

Conversely, if BD is identified, the BDP method may further comprise removing the BD and recovering data using a matrix completion method.

The BDP method may further comprise performing additional SE based on recovered SCADA and PMU measurements.

The performing of the additional DSE may comprise generating new SCADA-based and PMU-based estimates by performing ADMM-based DSE in parallel.

The BDP method may further comprise, after the performing of the additional DSE, fusing the estimation results of the SCADA-based DSE and the PMU-based DSE algorithms.

The BDP method may further comprise, before the performing of the phasor-aided normalized residual test and the general normalized residual test, establishing extended state vectors and an extended state vector set for each sub-area.

The BDP method may further comprise, after the establishing of the extended state vectors and the extended state vector set, performing the SCADA-based and the PMU-based DSE algorithms in parallel, wherein the performing of these algorithms may comprise generating SCADA-based and PMU-based estimated states of each sub-area while interacting with an adjacent estimator.

A device for performing a BDP method according to yet another aspect of the present invention for resolving the above-described technical objectives may comprise, as a device for performing a BDP method in H-DSE for monitoring an operating state of a large-scale power system, a memory that is configured to store at least one command; and a processor connected to the memory and configured to execute the at least one command, wherein, when the processor operates, the at least one command causes the processor to perform operations of: mixing estimation results of SCADA-based and PMU-based DSE algorithms and performing a phasor-aided normalized residual test and a general normalized residual test; and determining whether there is BD in SCADA and PMU measurements for each sub-area, wherein each sub-area is distinguished or defined by a network topology related to the SCADA and the PMU measurements and position information of measurement equipment according to the network topology.

After the determining of whether there is BD in the SCADA and the PMU measurements for each sub-area, the at least one command may cause the processor to further perform operations of: when there is BD, removing the BD and recovering data using a matrix completion method; performing additional DSE using SCADA and PMU data recovered in the recovering of the data wherein the performing of the additional DSE comprises performing ADMM-based DSE to generate additional SCADA and PMU measurements; and fusing the results of the SCADA-based and the PMU-based DSE algorithms.

After the determining of whether there is BD in the SCADA and the PMU measurements for each sub-area, the at least one command may cause the processor to further perform an operation of fusing the results of the SCADA-based and the PMU-based DSE algorithms when there is no BD.

Before mixing of the estimation results of the SCADA-based and the PMU-based DSE algorithms, and performing of the phasor-aided normalized residual test and the general normalized residual test, the at least one command may cause the processor to further perform the following step: receiving a network topology related to the SCADA and the PMU measurements, and position information of each piece of measurement equipment; establishing extended state vectors and an extended state vector set for each sub-area; performing the SCADA-based DSE algorithm using the SCADA measurement for each sub-area and a covariance matrix of integrated vectors of the SCADA and the PMU measurements; performing the PMU-based DSE algorithm using the PMU measurement for each sub-area and the covariance matrix of the integrated vectors of the SCADA and the PMU measurements in parallel with the SCADA-based DSE algorithm.

Exemplary embodiments of the present disclosure are disclosed herein. However, specific structural and functional details disclosed herein are merely representative for purposes of describing exemplary embodiments of the present disclosure. Thus, exemplary embodiments of the present disclosure may be embodied in many alternate forms and should not be construed as limited to exemplary embodiments of the present disclosure set forth herein.

Accordingly, while the present disclosure is capable of various modifications and alternative forms, specific exemplary embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that there is no intent to limit the present disclosure to the particular forms disclosed, but on the contrary, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure. Like numbers refer to like elements throughout the description of the figures.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (i.e., “between” versus “directly between,” “adjacent” versus “directly adjacent,” etc.).

The terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this present disclosure belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Hereinafter, exemplary embodiments of the present disclosure will be described in greater detail with reference to the accompanying drawings. In order to facilitate general understanding in describing the present disclosure, the same components in the drawings are denoted with the same reference signs, and repeated description thereof will be omitted.

is a conceptual diagram illustrating a DPHASE method which may be employed in a H-DSE algorithm according to an embodiment of the present invention.

Referring to, according to a H-DSE algorithm, SCADA measurement Zfor a specific sub-area k and a covariance matrix Rof the SCADA measurement are input so that SCADA-based DSE algorithm is performed (S). In parallel with SCADA-based DSE, PMU measurement Zand a covariance matrix Rof the PMU measurement are input so that PMU-based DSE algorithm is performed (S).

SCADA measurement Zand PMU measurement Zmay be referred to as a SCADA measurement value and a PMU measurement value, respectively. DSE may be a simple expression of a distributed structure or distribution of SE.

Performing parallel SCADA-based DSE and PMU-based DSE involves a SCADA-based estimator and a PMU-based estimator obtaining SE results in parallel for each sub-area k using the SCADA measurement Zand the PMU measurement Z, respectively. In addition, performing parallel SCADA-based and PMU-based DSE algorithms may involve an estimator of each sub-area interacting with an adjacent estimator and generating SCADA and PMU distribution states of each sub-area in parallel using ADMM-based DSE. Since the ADMM-based DSE does not employ a central estimator, parallel computing can be easily applied thereto. In other words, an estimator of each sub-area can share a SE value with an adjacent estimator in an overlapping sub-area between adjacent sub-areas and thus can have expandability.

Subsequently, cross-validation between the SCADA data and the PMU data is performed through a phasor-aided normalized residual test (S).

In the cross-validation, normalized residuals are obtained by dividing conventional residuals by the square roots of diagonal elements in a covariance matrix of general residuals or the conventional residuals. Then, conventional residuals are redefined as differences between SCADA measurements and estimations of SCADA measurements employing PMU SE. Here, the SCADA measurements employing PMU SE are calculated using a SCADA-based measurement function and PMU-based estimation.

Also, SCADA estimation may correspond to estimation of SCADA measurement, SCADA-based estimation, or SCADA SE, and PMU estimation may correspond to estimation of PMU measurement, PMU-based estimation, or PMU SE. Here, only measurement functions corresponding to SCADA estimation and PMU estimation are applied. In other words, the measurement functions to be applied may include a first function representing that a value obtained by subtracting a SCADA-based measurement function for SCADA SE from a SCADA measurement is equal to a SCADA conventional residual, and a second function representing that a value obtained by subtracting a Jacobian matrix of a SCADA-based measurement function for PMU SE from a PMU measurement is equal to a PMU conventional residual. The Jacobian matrix of a SCADA-based measurement function is calculated on the assumption that a SCADA state vector is equal to a SCADA SE.

According to the cross-validation, a PHASE method may identify all measurements with a residual larger than a threshold value as BD.

Subsequently, it is determined whether there is BD in SCADA-based DSE and PMU-based DSE for the specific sub-area k (S).

The cross-validation operation Sand the BD determination operation Smay correspond to a BD detection and identification process.

When it is determined in the BD determination operation Sthat there is BD (Yes in S), BD correction and re-estimation are performed (S). The BD correction and re-estimation may include an operation of removing BD and recovering data similar to an actual value using a matrix completion method and the like.